The present invention is related to computer vision, and in particular to object detection and recognition using computer vision.
Computer vision techniques for providing object detection and recognition are employed in a plurality of applications such as video surveillance. Object detection refers to computer vision techniques employed to detect the presence or absence of an object within an image. Object recognition refers to computer vision techniques for recognizing/identifying detected objects. For example, object detection techniques may be used to identify the presence of a person in an image, wherein object recognition techniques would be used to detect the identity of the detected person.
Prior art methods of computer vision object detection typically employ either global object detection techniques or local object detection techniques. Global object detection refers generally to computer vision techniques for detecting major objects from entire images or sequence of images. For example, global object detection techniques allow a computer to detect the presence of objects such as people or vehicles within a particular image. Local object detection employs processes that analyze local aspects of an image to detect and recognize objects. In general, local object detection is more useful for identifying/recognizing objects. For example, local object detection and recognition may be employed to recognize facial features employed to identify a particular person.
However, the performance of local object detection algorithms decreases in uncooperative environments, such as those in which there are resolution, frame rate, illumination, or obscuration issues. Prior art solutions to these problems focus on how to enhance the quality of the detected images (i.e., improve the resolution, illumination, etc.) in order to improve the quality of the local object detection. While useful, these tools are not always successful.